feat: new structure

src/apis/jina_cloud.py

@@ -0,0 +1,258 @@
+import hashlib
+import json
+import os
+import re
+import subprocess
+import threading
+import webbrowser
+from pathlib import Path
+
+import click
+import hubble
+from hubble.executor.helper import upload_file, archive_package, get_request_header
+from jcloud.flow import CloudFlow
+from jina import Flow
+
+from src.utils.io import suppress_stdout, is_docker_running
+from src.utils.string_tools import print_colored
+
+
+import requests
+import time
+
+def wait_until_app_is_ready(url):
+    is_app_ready = False
+    while not is_app_ready:
+        try:
+            response = requests.get(url)
+            print('waiting for app to be ready...')
+            if response.status_code == 200:
+                is_app_ready = True
+        except requests.exceptions.RequestException:
+            pass
+        time.sleep(0.5)
+
+def open_streamlit_app():
+    url = "http://localhost:8081/playground"
+    wait_until_app_is_ready(url)
+    webbrowser.open(url, new=2)
+
+
+def redirect_callback(href):
+    print(
+        f'You need login to Jina first to use GPTDeploy\n'
+        f'Please open this link if it does not open automatically in your browser: {href}'
+    )
+    webbrowser.open(href, new=0, autoraise=True)
+
+
+def jina_auth_login():
+    try:
+        hubble.Client(jsonify=True).get_user_info(log_error=False)
+    except hubble.AuthenticationRequiredError:
+        print('You need login to Jina first to use GPTDeploy')
+        print_colored('', '''
+If you just created an account, it can happen that the login callback is not working.
+In this case, please cancel this run, rerun your gptdeploy command and login into your account again. 
+''', 'green'
+              )
+        hubble.login(prompt='login', redirect_callback=redirect_callback)
+
+
+def push_executor(dir_path):
+    dir_path = Path(dir_path)
+
+    md5_hash = hashlib.md5()
+    bytesio = archive_package(dir_path)
+    content = bytesio.getvalue()
+    md5_hash.update(content)
+    md5_digest = md5_hash.hexdigest()
+
+    form_data = {
+        'public': 'True',
+        'private': 'False',
+        'verbose': 'True',
+        'md5sum': md5_digest,
+    }
+    with suppress_stdout():
+        req_header = get_request_header()
+
+    resp = upload_file(
+        'https://api.hubble.jina.ai/v2/rpc/executor.push',
+        'filename',
+        content,
+        dict_data=form_data,
+        headers=req_header,
+        stream=False,
+        method='post',
+    )
+    json_lines_str = resp.content.decode('utf-8')
+    if 'exited on non-zero code' not in json_lines_str:
+        return ''
+    responses = []
+    for json_line in json_lines_str.splitlines():
+        if 'exit code:' in json_line:
+            break
+
+        d = json.loads(json_line)
+
+        if 'payload' in d and type(d['payload']) == str:
+            responses.append(d['payload'])
+        elif type(d) == str:
+            responses.append(d)
+    return '\n'.join(responses)
+
+
+def get_user_name():
+    client = hubble.Client(max_retries=None, jsonify=True)
+    response = client.get_user_info()
+    return response['data']['name']
+
+
+def _deploy_on_jcloud(flow_yaml):
+    cloud_flow = CloudFlow(path=flow_yaml)
+    return cloud_flow.__enter__().endpoints['gateway']
+
+
+def deploy_on_jcloud(executor_name, microservice_path):
+    print('Deploy a jina flow')
+    full_flow_path = create_flow_yaml(microservice_path, executor_name, use_docker=True)
+
+    for i in range(3):
+        try:
+            host = _deploy_on_jcloud(flow_yaml=full_flow_path)
+            break
+        except Exception as e:
+            print(f'Could not deploy on Jina Cloud. Trying again in 5 seconds. Error: {e}')
+            time.sleep(5)
+    if i == 2:
+        raise Exception('''
+            Could not deploy on Jina Cloud. 
+            This can happen when the microservice is buggy, if it requires too much memory or if the Jina Cloud is overloaded. 
+            Please try again later.
+'''
+        )
+
+    print(f'''
+Your Microservice is deployed.
+Run the following command to start the playground:
+
+streamlit run {os.path.join(microservice_path, "app.py")} --server.port 8081 --server.address 0.0.0.0 -- --host http://{host}
+'''
+          )
+    return host
+
+def run_streamlit_app(app_path):
+    subprocess.run(['streamlit', 'run', app_path, 'server.address', '0.0.0.0', '--server.port', '8081', '--', '--host', 'grpc://localhost:8080'])
+
+
+def run_locally(executor_name, microservice_version_path):
+    if is_docker_running():
+        use_docker = True
+    else:
+        click.echo('Docker daemon doesn\'t seem to be running. Trying to start it without docker')
+        use_docker = False
+    print('Run a jina flow locally')
+    full_flow_path = create_flow_yaml(microservice_version_path, executor_name, use_docker)
+    flow = Flow.load_config(full_flow_path)
+    with flow:
+        print(f'''
+Your microservice started locally.
+We now start the playground for you.
+''')
+
+        app_path = os.path.join(microservice_version_path, "app.py")
+
+        # Run the Streamlit app in a separate thread
+        streamlit_thread = threading.Thread(target=run_streamlit_app, args=(app_path,))
+        streamlit_thread.start()
+
+        # Open the Streamlit app in the user's default web browser
+        open_streamlit_app()
+
+        flow.block()
+
+def create_flow_yaml(dest_folder, executor_name, use_docker):
+    if use_docker:
+        prefix = 'jinaai+docker'
+    else:
+        prefix = 'jinaai'
+    flow = f'''
+jtype: Flow
+with:
+  name: nowapi
+  port: 8080
+jcloud:
+  version: 3.14.2.dev18
+  labels:
+    creator: microchain
+  name: gptdeploy
+
+executors:
+  - name: {executor_name.lower()}
+    uses: {prefix   }://{get_user_name()}/{executor_name}:latest
+    {"" if use_docker else "install-requirements: True"}
+    jcloud:
+      resources:
+        instance: C2
+        capacity: spot
+'''
+    full_flow_path = os.path.join(dest_folder,
+                                  'flow.yml')
+    with open(full_flow_path, 'w') as f:
+        f.write(flow)
+    return full_flow_path
+
+
+def replace_client_line(file_content: str, replacement: str) -> str:
+    lines = file_content.split('\n')
+    for index, line in enumerate(lines):
+        if 'Client(' in line:
+            lines[index] = replacement
+            break
+    return '\n'.join(lines)
+
+
+def update_client_line_in_file(file_path, host):
+    with open(file_path, 'r') as file:
+        content = file.read()
+
+    replaced_content = replace_client_line(content, f"client = Client(host='{host}')")
+
+    with open(file_path, 'w') as file:
+        file.write(replaced_content)
+
+
+def process_error_message(error_message):
+    lines = error_message.split('\n')
+    relevant_lines = []
+
+    pattern = re.compile(r"^#\d+ \[[ \d]+/[ \d]+\]")  # Pattern to match lines like "#11 [7/8]"
+    last_matching_line_index = None
+
+    for index, line in enumerate(lines):
+        if pattern.match(line):
+            last_matching_line_index = index
+
+    if last_matching_line_index is not None:
+        relevant_lines = lines[last_matching_line_index:]
+
+    return '\n'.join(relevant_lines[-25:])
+
+
+def build_docker(path):
+    # The command to build the Docker image
+    cmd = f"docker build -t micromagic {path}"
+
+    # Run the command and capture the output
+    process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
+    stdout, stderr = process.communicate()
+
+    # Check if there was an error
+    if process.returncode != 0:
+        error_message = stderr.decode("utf-8")
+        relevant_error_message = process_error_message(error_message)
+        return relevant_error_message
+    else:
+        print("Docker build completed successfully.")
+        return ''